The medical field is undergoing a revolutionary transformation, driven by two cutting-edge technologies: Artificial Intelligence (AI) and 3D printing. When these forces collide, they unleash unparalleled potential for innovation, personalization, and improved patient outcomes. From custom prosthetics to intricate anatomical models for surgical planning, the synergy between AI and 3D printing is reshaping healthcare as we know it. Let’s dive into some of the latest advancements, drawing insights from pioneering researchers and practitioners at the forefront of this exciting intersection.

Personalized Prosthetics for a Better Quality of Life

Creating personalized prosthetics is one of the most compelling applications of AI and 3D printing in healthcare. Merel van der Stelt, a PhD student at 3D Lab Radboudumc, is dedicated to developing prostheses for low- and middle-income countries, utilizing AI for optimized socket shape design (LinkedIn activity). By leveraging AI algorithms, researchers can analyze individual patient data, such as limb shape and movement patterns, to create prosthetics that fit perfectly and function seamlessly. This level of customization enhances comfort, mobility, and overall quality of life for those in need, especially in regions with limited resources.

Moreover, Johann Reinhard, a Research Scientist at Fraunhofer IGD, is pushing the boundaries of 3D printing for eye implants. Using AI to design prints from optical coherence tomography (OCT) images, researchers can create bespoke prosthetic eyes that closely match the patient’s anatomy (New Scientist article). Imagine a future where individuals with eye injuries or congenital disabilities can receive custom-made implants that restore both function and aesthetics. This level of precision and personalization is made possible by AI, which can analyze complex medical images and generate intricate 3D designs.

Enhancing Surgical Planning and Precision

Surgical planning is another area where the combination of AI and 3D printing is making significant strides. Kaiyan Qiu, an assistant professor at Washington State University, uses AI to determine optimal 3D printing parameters for creating surgical planning organ models (WSU press release). Surgeons can generate highly accurate 3D models of patient-specific organs by feeding medical imaging data into AI algorithms. These models allow for detailed pre-operative planning, enabling surgeons to visualize complex anatomical structures and practice procedures before stepping into the operating room. This reduces surgical time and risk and improves patient outcomes by ensuring greater precision and predictability.

Furthermore, Gade Jong, another Assistant Professor, focuses on AI and 3D technologies for anatomical segmentation (Nature article). Precise segmentation of organs and tissues from medical images is crucial for creating accurate 3D models. AI algorithms can automate and refine this process, allowing quicker and more reliable generation of models used in surgical planning. By automating these complex processes, AI enables healthcare professionals to be more efficient and reduce the risk of human error.

Innovations in Bioprinting and Tissue Engineering

Bioprinting, the process of 3D printing living tissues and organs, is a realm where AI is proving invaluable. Ben Kiratitanaporn, a recent PhD graduate, is utilizing AI for 3D cell scaffolds. Cell scaffolds provide the structural support for cells to grow and form functional tissues. AI can optimize the design of these scaffolds, ensuring the proper porosity, mechanical properties, and biocompatibility. Similarly, Guo-Dong Goh, a Research Fellow, focuses on AI for 3D printing tissue-like anatomical models and anomaly detection (ScienceDirect article). AI algorithms can analyze the quality of printed tissues, detecting defects or inconsistencies that might compromise their function.

Associate Professors like Stefanie Mueller at MIT explore human-computer interaction technologies fabricated using 3D printing and AI (MIT news), while Washburn Lab at Carnegie Mellon develops AI for choosing design parameters in the bioprinting of hydrogels (ACS Biomaterials article). Additionally, Demaria at Centro Piaggio, University of Pisa, uses AI to select printing parameters for bioprinting (IJB article). These collective efforts are pushing the boundaries of what’s possible, making the creation of functional tissues and organs closer to reality than ever before.

Drug Delivery and Microneedle Technology

The intersection of AI and 3D printing also opens up exciting possibilities in drug delivery. Moe Elbadawi, a Lecturer, investigates AI, 3D printing, and drug delivery, building upon the work of Alvaro Goyanes at FABRX (ScienceDirect article). By 3D printing personalized drug formulations and delivery devices, we can ensure that patients receive the proper medication in the correct dosage, tailored to their unique needs. This precision approach can improve treatment efficacy and minimize side effects.

Furthermore, Stasoglu at Koç University is leveraging AI to tune 3D printing parameters for microneedle design (MDPI article). Microneedles offer a painless and efficient way to deliver drugs through the skin. AI can optimize the design and fabrication of these tiny needles, enhancing their effectiveness and patient comfort.

Advancements in Wearable Technology and Physiological Monitoring

Wearable technology is another area benefiting from the AI-3D printing synergy. Wei Gao, a professor at Caltech, developed 3D-printed electronic wearable skin that uses AI for physiological monitoring (Science article). These innovative devices can continuously track vital signs and other health indicators, providing valuable data for personalized medicine and early disease detection. Imagine a world where wearable sensors seamlessly integrate with the body, continuously monitoring health and sending alerts when needed.

Defect Detection and Quality Control

Amedeo Bonatti (ORCID) uses AI, particularly expert systems, for defect detection in electron beam melted implants (Liebertpub article). Ensuring the quality and structural integrity of 3D-printed medical devices is critical for patient safety. AI algorithms can analyze 3D-printed objects in detail, identifying any defects or anomalies that might compromise their function. This level of quality control ensures that medical devices meet the highest standards of safety and efficacy.

Devin Roach at Oregon State University also applies AI to 3D printing for biomedical applications. His co-authored article titled “Invertible Neural Networks for Real-Time Control of Extrusion Additive Manufacturing” explores the application of machine learning, specifically invertible neural networks (INNs), to enhance the precision and adaptability of direct ink write (DIW) 3D printing processes. This research is particularly relevant to healthcare due to its potential to improve the fabrication of customized medical devices and implants.(ScienceDirect) Integrating INNs allows for real-time monitoring and optimization of the printing process, ensuring that these medical products meet stringent quality and performance standards. By enabling adaptive control during fabrication, this approach can lead to more reliable and efficient production of complex biomedical structures, ultimately enhancing patient outcomes.

The Future Outlook

The advancements discussed above represent the exciting possibilities when AI and 3D printing intersect in healthcare. As these technologies evolve, we can expect even more groundbreaking innovations. The ability to create personalized medical devices, plan complex surgeries precisely, bioprint functional tissues, and develop advanced drug delivery systems will revolutionize patient care. The integration of AI enhances this process through analysis, automation, and control to ensure that we receive the highest level of patient care. This exciting field has tremendous potential to transform the healthcare landscape and improve lives worldwide.

To learn more about these exciting developments, don’t miss our upcoming virtual event focusing on this very topic.

Keywords: AI in healthcare, 3D printing, personalized medicine, bioprinting, surgical planning, drug delivery, wearable technology.

Related links:

• Artificial Intelligence and Machine Learning in 3D Printing, AR/VR Explores how AI and machine learning are optimizing design, predictive maintenance, quality control, and patient-specific modeling for 3D-printed medical devices and implants. The article also discusses how AI/ML can reduce costs and improve patient outcomes.
• Artificial Intelligence in Healthcare 3D Printing (Webinar) Recap and resources from a theme-based webinar featuring industry and academic leaders discussing the latest AI-driven advancements in healthcare 3D printing.
• When Artificial Intelligence Meets 3D Printing An in-depth article explaining the basics of AI and machine learning, their relevance to 3D printing, and the challenges of integrating AI into real-time 3D printing monitoring and quality control.

Additional 3DHEALS Resources:

1. https://3dheals.com/product/artificial-intelligence-and-machine-learning-in-3d-printing-ar-vr/
2. https://3dheals.com/machine-learning-in-3d-printing-and-bioprinting-a-collection-of-recent-publications/
3. https://3dheals.com/artificial-intelligence-for-segmentation/
4. https://3dheals.com/product/artificial-intelligence-in-healthcare-3d-printing/
5. https://3dheals.com/courses/artificial-intelligence-in-healthcare-3d-printing/
6. https://3dheals.com/artificial-intelligence-updates-for-3d-printing-and-bioprinting/
7. https://3dheals.com/when-artificial-intelligence-meets-3d-printing/
8. https://3dheals.com/tag/machine-learning/
9. https://3dheals.com/artificial-intelligence-and-3d-printing/
10. https://www.youtube.com/c/3DHEALSINNOVATION
11. https://3dheals.com/interview-with-kerim-genc-the-power-of-artificial-intelligence-and-3d-printing/

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(Photo Credit above: Dr. Tim Anderson)

Want to write a piece for 3DHEALS Expert Corner? Email us: info@3dheals.com

Artificial Intelligence (AI) is the leading field of science nowadays.
Machines can be programmed to learn and complete tasks without human supervision. 

In other words, artificial intelligence is a self-learning system that can work with specific problems and make independent intelligent decisions (Chace, 2018). AI is applied to various fields of modern technologies and can be implemented to the manufacturing industry. The most innovative way of production is additive manufacturing, especially 3D printing.

The best advantage of 3D printing is unsupervised complex manufacturing. Various polymer, metal, and biomaterials are used in engineering applications mainly to create products with unique shapes, multifunctional compositions, reliability, and high quality. The 3D printing includes various techniques, but the most useful and time-tested method is Stereolithography (SLA). 

STEREOLITHOGRAPHY

SLA is the 3D printing process in which ultraviolet laser shoots on the surface of a tank filled with the photopolymer liquid. Energy, transferred by the laser to the material, activates curing reaction, which leads to the solidifying of the pattern traced on the photopolymer. Next, the build platform moves on the distance equal to the thickness of the one layer (Varotsis, 2018). The next layer is cured joining the previous layer. This procedure is repeated until the object is finished. However, every manufacturing method has its own problems and the risk of product failure.

REASONS OF THE SLA FAILURE 

What can cause failure during the SLA 3D printing process?
There are several reasons including:

• Photopolymer material failure 
• The UV-laser wavelength or scanning intensity change
• Curing (photopolymerization) reaction violation

These problems can be solved with the help of artificial intelligence.

INTRODUCTION TO PHOTOPOLYMERIZATION

Material, which is used in SLA, is called photopolymer and the name of the curing reaction is photopolymerization.

Steps of reaction (Terselius, 1998):

1. Radical formation: Radicals are formed under the exposure of the UV light
2. Propagation: energized photoinitiators create potential bonds
3. Termination: ends of the polymer chains face each other resulting in the rapid growth of the polymer chain

In the end, active groups are not able to create new bonds anymore, which means that the polymer chain grows process is terminated. 

PHOTOPOLYMERIZATION REACTION PROBLEMS AND SOLUTIONS

Resin needs the right amount of energy to achieve solidification. If the material receives not enough UV energy or the laser is spending less time for the curing process, then the print will not have appropriate characteristics to meet the application requirements (Jennings, 2018). The most optimal solution is to decrease the speed of printing by modifying laser settings (Jennings, 2018).

The second problem is associated with the lack of an appropriate amount of energy needed for the curing process (Jennings, 2018). However, an  AI system can modify the settings of the laser by increasing energy gradually to avoid abrupt changes during the 3D printing process.

Strength of the photopolymer can be increased by adjusting the laser in two ways (Ligon, et al., 2017): 

• Lowering the penetration depth
• Increasing the amount of energy

The penetration depth is the depth to which laser penetrates the photopolymer material layer and defined by (Ligon, et al., 2017):

Dp=Penetration depth  ε=Molar extinction coefficient  I=Photoinitiator concentration 

Penetration depth is reduced by adding light absorbers which change the formula (Ligon, et al., 2017):

I;A=Extinction coefficient  I=Photoinitiator concentration  A=Concentration of the absorber

UV absorbers increase the building time, but they improve resolution and strength. Penetration depth reduction is extremely important for the improvement of the resolution allowing the creation of thinner layers.

Critical exposure Ec is the energy needed to start the solidification reaction, which is defined as (Ligon, et al., 2017):

Ec=Critical exposure E0=Energy amount on the surface Cd=Curing depthDp=Penetration depth 

Formulas can be used to create an equation system for further implementation in the program of adjustment of the UV laser settings to control the material curing reaction. The aim is to achieve the right amount of energy which is needed to obtain successful solidification and meet the optimal properties of the final product.

WORKING CONCEPT OF AI

Possible concept of AI implementation in SLA includes layer scanning system, collection of the information, analysis, and solution to fix the failure without interruption of the 3D printing process (Bharadwaj, 2018). 

The failure is fixed at the earliest stage. The machine identifies divergence from the design and solves the problem as soon as the failure starts to appear. Therefore, the sensitivity of the scanning system and the reaction of the machine defining the errors should be developed.

The other way to fix production failure is to create a 3D printer that could remove material from the failed region. 

Next, AI should analyze the problem and find another way to build the part without changing final product properties.

The possible set of equipment for the creation of such technology includes:

• SLA 3D printer
• Sensors
• Scanning cameras
• Focused laser beam
• Machine learning algorithm
• Software

The software for the machine learning system is created with the machine coding which is very primitive, but complex at the same time. Machine code is the native code that a machine can read and execute to complete the specific task (Rouse, 2018). 

Additive manufacturing is based on the adding material layer by layer. 3D printers do not remove material layers. However, the failure happens in the printed layer which can be removed, predicted or enhanced.

In the first case, the material should be removed with high accuracy. The tool which can be used for such an application is the focused laser beam (Peels, 2017). Technology requires a laser that can move in three-dimensional space. The laser should be parallel to the printed layers to cut each layer from the side. Problematic layers should be analyzed by the scanning system and removed, interrupting the printing for a very short period of time, then the printing process should be continued. Despite the fact that the print can fail many times, the product will be successfully finished, and the machine will learn a lot from the problematic print at the end. The learning algorithm is enclosed, and the program will be repeated until reaching a successful result.

AI systems should be created to analyze large sets of data and make an instant decision, whereas humans are not able to react faster than the computer, just in a few seconds.

The system should repeat the process of material removing before the machine will learn how to fix the problem. This procedure improves the fixing process with prediction analysis and decreases the probability of the failure.

CONCLUSION

Machine learning improves the printing quality reducing risks of failure and manufacturing waste. The recycling in the field of additive manufacturing should be minimized with zero waste production with the implementation of AI. Also, there are a lot of possible ways that can be developed to protect the printing data and digital security system due to AI technologies.

REFERENCES

Chace, C., 2018. Artificial Intelligence and the Two Singularities. 1st ed. Boca Raton: CRC Press.

Varotsis, A. B., 2018. Introduction to SLA 3D Printing. [Online]
Available at: https://www.3dhubs.com/knowledge-base/introduction-sla-3d-printing#author
[Accessed 12 June 2018].

Terselius, B., 1998. Introduction to Polymer Science. 1st ed. Kristianstad: Arkitektkopia S. Niklasson AB.

Jennings, A., 2018. 3D Printing Troubleshooting Guide: 41 Common Problems. [Online]
Available at: https://all3dp.com/1/common-3d-printing-problems-troubleshooting-3d-printer-issues/
[Accessed 6 October 2018].

Ligon, S. C. et al., 2017. Polymers for 3D Printing and Customized Additive Manufacturing. Chemical Reviews, 117(15), pp. 10212-10290.

Bharadwaj, R., 2018. Artificial Intelligence Applications in Additive Manufacturing (3D Printing). [Online]
Available at: https://www.techemergence.com/artificial-intelligence-applications-additive-manufacturing-3d-printing/
[Accessed 12 September 2018].

Rouse, M., 2018. Machine code (machine language). [Online]
Available at: https://whatis.techtarget.com/definition/machine-code-machine-language
[Accessed 17 September 2018].

Peels, J., 2017. Comparison of Metal 3D Printing — Part Two: Directed Energy Deposition. [Online]
Available at: https://3dprint.com/182367/directed-energy-deposition/
[Accessed 6 August 2018].

FIGURES

Figure 1. Schematic of an SLA 3D printer (Varotsis, 2018).https://www.3dhubs.com/knowledge-base/introduction-sla-3d-printing/#author

Figure 2. Polymerization (MIT, 2018).https://formlabs.com/fr/blog/guide-ultime-impression-3D-stereolithographie-sla/

Figure 3. Relation between laser intensity, voxel size, and success of polymerization (Ligon, 2017).https://www.ncbi.nlm.nih.gov/pubmed/28756658

Figure 4. Representation of gel curing profiles. Laser penetrates deeply but only lightly cross-links the gel. (Jim H. Lee, et al., 2001).https://www.princeton.edu/~cml/assets/pdf/0112lee_curing.pdf

About the Author:

Reino Iuganson graduate student from Arcada university with a Bachelor’s Degree in Materials Engineering (Helsinki, Finland). He started his work with the injection molding industry in 2016 at the Plastoco company, then developed a prototype for a product design project in collaboration between Arcada university and Laerdal medical company using CAD/CAM and 3D printing. He is currently working on the project Artificial Intelligence in 3D printing.

Related Articles:

When Artificial Intelligence Meets 3D Printing

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Mixed Reality and 3D Printing, Imperfections to Perfection?

From Academia: 3D Printed Pills, 4D Printed Structure, and how to property 3D print Chocolate

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From Academia: 3D Printing and Robotics, Stem cell coated Implants, Decentralized Mitigation of Pandemics

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Want to write a piece for 3DHEALS Expert Corner? Email us: info@3dheals.com

There are several main reasons that frequently motivate the innovators:

• Do cool things that could not be done before (e.g. flying, electricity, etc.).
• Make life better by a magnitude of a million times, etc. and not just minor increments (e.g. discovery of antibiotics).
• Save time, labor, and money that would recreate the industrial revolution and new economies.

While the general public is fascinated with both artificial intelligence and 3D printing as powerful new technological tools, and their potential future impact in healthcare, there has not been any known “killer applications” that utilize AI to improve existing 3D printing applications, in or out of healthcare/life sciences. The easy answer could be that both technologies are still relatively new, or that people who focus on AI applications are not necessarily interested in 3D printing, and vice versa. Or, maybe it’s because we simply do not have enough solutions to problems at hand.

Some of the proposed ways AI can improve 3D printing include the following [1-7]:

• Improve prefabrication design process
• Defect/Failure Detection
• Real-Time 3D printing Control/Failure compensation
• Predictive Maintenance/Inventory
• Workflow (Cost) optimization
• Chemical reaction/photopolymerization using ML-based algorithm to maximize control (chemicals and energy input)

There is an interesting analogy that I came across from professor Hiroya Tanaka, [2] with the following image(Figure 1). This shows that the subject “3D printing” has the visible physical components (tip of the iceberg) and the much larger invisible components in the realm of software, including data science, advanced 3D modeling, 3D object storage and retrieval, and AI/ML/Deep learning. While this is in accordance with the belief that “software eats the world” by the Silicon Valley, I would argue that all of these components will be equally important to the achieve the theoretical promises 3D printing as a successful manufacturing alternative.

That said, it is still helpful to do a brief review of where we are in terms of the intersection of these two technologies. Hopefully, this article can inspire interesting discussions, and even better, some new startups that Pitch3D can host very soon.

Artificial Intelligence/Machine Learning/Deep Learning

Artificial intelligence is an “intelligence” that is demonstrated by machines, which can perceive its environment and take actions to maximize its chance of success through the “learning” and “problem-solving” process. Machine learning is the scientific study of algorithms and statistical models that computers use to perform a specific task without human instructions, relying on patterns and inference instead. There are unsupervised ML (no human input) and supervised ML (human input). Finally, deep learning, also known as hierarchical learning, is based on artificial neural networks. There are also supervised and unsupervised DL.

The relationships among the concepts of artificial intelligence, machine learning, and deep learning (using artificial neural networks) are best demonstrated in the following diagram. (There are more sub-categories within each of these concepts that interested readers can easily find on the internet.)

The Problems

It is my theory that inventors can be lucky, but the inventions are never accidental. Inventions that changed human history (e.g. robots, computers, 3D printers, microbiology) are results of the continuous search for answers over long periods of time, from different perspectives and angles, and sometimes only after thousands of years.

The current status of healthcare applications using 3D printing is not so favorable because of several reasons:

1. 3D printing is still expensive, not just from the hardware and material cost, but also labor cost, and waste due to print defects and failures.
2. Lack of efficient and affordable design software. This is, in particular, a problem for the healthcare sector.
3. 3D printing is unable to achieve affordable (customized) mass production due to workflow challenges.
4. Lack of good quality control processes and tools, especially for the heavily regulated healthcare sectors.

The list can go on.

However, challenges also present opportunities, and AI/ML seem to be potential solutions to these worthy problems because AI/ML do somethings better than humans in many ways:

• Computers are able to process large amounts of data, learn, and implement actions in a more consistent fashion.
• Computers require little resources to function (i.e. electricity, minimal to no need for human operation).
• Computers can function well even in a toxic or harsh environment. (e.g. high temperature, toxic fumes)
• “Skillset” (algorithms) can be more rapidly “learned” and disseminated in a consistent way than human learning.
• Computers can store and retrieve large amounts of information almost instantaneously.

That said, creating the right AI/ML algorithm to 3D printing is no easy task because of the following:

• Successful AI/MI for the 3D printing process requires extensive knowledge of the specific 3D printing technologies, including but not limited to the design process, control of machine components, material science, post-processing. For example, the strategies behind optimizing the SLA based 3D printing process [1] will be very different from laser sintering metal 3D printing. [4]
• Finding high-value problems based on the end goal of production.  Either it is focused on reducing wasted time or precious materials, or ensuring end product mechanical properties that could result in serious clinical outcomes.  [1]
• Data collection. For example, for 3D printed anatomical models, a good AI/ML product focusing on optimizing the segmentation process will significantly decrease the bottleneck effect of entering the field for many hospitals and clinics. However, the lack of such a product is because of a lack of enough training datasets. [3]
• Intrinsic limitations of existing monitoring systems. Researchers are currently using either photos or videos to train their AI/ML algorithms. Smoothly incorporating the monitoring systems without interrupting the printing process will be challenging. [1, 4, 5] However, such integration will be required to achieve “real-time” 3D printing monitoring and subsequent “fixing” or “failure compensation” of the prints. [1]
• Forming a successful team that can tackle problems along the entire 3D printing process from design to final product requires a group of people from different disciplines. [1] For example, to accomplish real-time SLA 3D printing support modification[Figure 3], Dr. Iuganson proposed in his thesis a team structure that would include the following:

1. 3D printing engineer
2. Sensors technician
3. Automation CT engineer
4. Laser and optics engineer
5. Machine learning specialist develops a set of steps for correction of the printing and generating supports if the problem is predicted.
6. Data scientist creates a code for the machine to change the design structure and generated supports
7. AI research scientist analyses and implements the information in the AI system to add a new feature of real-time control over the design and supports.

Now, imagine that everyone on this team has to understand what is going on and can also communicate effectively with one another!

The Solutions

Solutions seem to be coming, but just not here yet.

GE Additive, Sculpteo, Autodesk, and many more all appear to actively develop AI/ML-based solutions to optimize various value points of the 3D printing process. [6] Align Technology just announced a new AI/ML-based visualization/predictive tool SmileView based on 60 million patient datasets. (Align is also actively hiring AI/ML engineers.) [8] It is my hope that perhaps more entrepreneurs can venture into this exciting intersection of two powerful emerging technologies.

Perhaps this IS where we will find the “killer app” in 3D printing.

References:

1. Artificial Intelligence in 3D Printing (Thesis by Dr. Reino Iuganson)
2. Deep Learning for Advanced 3D Printing
3. The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning (Trevor J. Huff, Parker E. Ludwig & Jorge M. Zuniga) ISSN: 1743-4440 (Print) 1745-2422 (Online) Journal homepage: https://www.tandfonline.com/loi/ierd20
4. Machine Learning “Fixes” 3D-Printed Metal Parts—Before They’re Built
5. Automated Process Monitoring in 3D Printing Using Supervised Machine Learning
6. Artificial Intelligence Applications in Additive Manufacturing (3D Printing)
7. Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting
8. New Dental Product: SmileView from Align Technology

Related Articles:

From Academia: 3D Printing and Robotics, Stem cell coated Implants, Decentralized Mitigation of Pandemics

Five Reasons Cybersecurity Will Play a Critical Role in 3D Printing in Healthcare – Part 1

The Augmented Mind: How AR/VR will empower 3D Printing technology in bettering the real world.

Decentralized Healthcare — Part I. Cooler than Bitcoins, But What Is It?

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(Originally published on “Two Cents” by Jenny Chen, M.D.)

J.P.Morgan Healthcare Conference attracts thousands of healthcare startups and investors from all over the world to San Francisco each January and is possibly one of the most impactful healthcare technology investment forums in the world. The city is overwhelmed with a slew of startup founders, investors, and service providers, each with their own ambitions and hopes and their best games in networking. Every SF bar is occupied by corporate receptions, and even hotel lounges start to charge a hefty hourly fee so that people can simply sit down and have a meeting. (Secret: The Westfield Mall food court is free.)

In addition to the main conference hosted by JP Morgan, many other conferences with related themes also took place with the same goal of connecting healthcare startups and investors, and in between service providers. Some of the notable events include RESI (Redefining Early Stage Investment), many medtech events organized by WSGR, and high-level healthcare forums by McDermott Will & Emery. By Tuesday, I was already exhausted from all the networking, conferencing, and receptions, but I took as many notes as I could, and here are some trends and tips I observed. Hopefully, these will be helpful to the 3DHEALS community, which also hold our first successful JP Morgan reception with our partners at Nixon Peabody.

1. Less “Disruption” and more “Integration”.

Throughout all the conferences I attended, there is a definite trend towards a change in presentation. In the homeland of Disrupt SF (TechCrunch) and rebellious history of the bay area, the switch of attitudes shows a new level of humbleness and wisdom after years of attempted, and perhaps unsuccessful, “disruptive” healthcare innovations. There is a growing trend of startups being more “inclusive” towards healthcare providers throughout product development and a lot more emphasis on technology integration with existing healthcare workflow. This is especially obvious in the realm of AI (artificial intelligence) based startups, since replacing the healthcare workforce used to be a more touted endpoint.

2. Artificial Intelligence, or “Neural Learning”?

No, not 3D printing (or bioprinting). A.I. remains to be the buzzword in healthcare this year. But people in industries focusing on decentralized manufacturing, automation, and robotics should all take notes.

As one investor tweeted, “ After hearing “We use AI” 4,345,812 times at #jpm19, I just got told by a company “oh no we’re not just another AI company…we use neural learning” and my eyes rolled so far back in my head they popped out the back and rolled down market street. If seen please return them to me…” (@geoffclapp)

The fervor is fueled a general dissatisfaction with existing healthcare systems (anywhere in the world), a number of tech giant’s involvements lately, an economic drive of democratizing high-level skills to lower-skill workers, and a more well-defined FDA clearance pathway. An A.I. based startup can now reportedly get FDA 510(k) clearance 5–8 months. Reportedly. The main challenge to investors will now be over-valuation. The main challenge to startups will be real returns, from reimbursement to non-incremental clinical benefits.

3. Money from Asia

During JPM19, there appears to be a sudden surge of Chinese and Asian investors. Even in a city like SF, it is unusual. There were several sessions during RESI focusing on fundraising from China or Asia. China/Asia clearly has a surplus of capital, and U.S./European based startups seem to be the safer bets due better-defined political, legal, and economic structures. However, not all money is equal. Notably, CFIUS was repeatedly mentioned during several of my meetings. It is a government entity authorized to review investment/transactions involving foreign investment in the United States, in order to determine the effect of such transactions on the national security of the United States. Given the current volatile China-US relationship, and volatile international relationships in general, tread carefully is probably the best advice to startups and investors alike, since no one seems to know where the world is going next.

https://3dheals.com/decentralized-healthcare%e2%80%8a-%e2%80%8apart-i-cooler-than-bitcoins-but-what-is-it/

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